NDLI: Reinforcement Learning with a Supervisor for a Mobile Robot in a Real-world Environment

Content Provider	IEEE Xplore Digital Library
Author	Conn, K. Peters, R.A.
Copyright Year	2007
Description	Author affiliation: Vanderbilt Univ., Nashville (Conn, K.; Peters, R.A.)
Abstract	This paper describes two experiments with supervised reinforcement learning (RL) on a real, mobile robot. Two types of experiments were preformed. One tests the robot's reliability in implementing a navigation task it has been taught by a supervisor. The other, in which new obstacles are placed along the previously learned path to the goal, measures the robot's robustness to changes in environment. Supervision consisted of human-guided, remote-controlled runs through a navigation task during the initial stages of reinforcement learning. The RL algorithms deployed enabled the robot to learn a path to a goal yet retain the ability to explore different solutions when confronted with a new obstacle. Experimental analysis was based on measurements of average time to reach the goal, the number of failed states encountered during an episode, and how closely the RL learner matched the supervisor's actions.
Starting Page	73
Ending Page	78
File Size	375292
Page Count	6
File Format	PDF
ISBN	1424407893
DOI	10.1109/CIRA.2007.382878
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Mobile robots Orbital robotics Testing Navigation State-space methods Computational modeling Computational intelligence Robotics and automation USA Councils Q-learning Reinforcement learning mobile robots
Content Type	Text
Resource Type	Article

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